Experience validating enterprise BI solutions aligned to Kimball dimensional modeling (facts, dimensions, conformed dimensions, SCD handling).
Proficiency validating end-to-end data pipelines and data transformations using Databricks (Delta tables, notebooks, jobs, workflows) and SQL transformations (Spark SQL / T-SQL).
Skill in developing test data, test cases, and testing plans; experience using AI tools to assist test design; ability to automate QA activities to improve release velocity.
Ability to define QA standards, guidelines, and best practices for analytics engineering and reporting teams, with willingness to mentor peers.
Requirements:
Perform end-to-end QA across ingestion → transformation → semantic layer → reporting/consumption, ensuring accuracy of business rules, KPIs, and reconciliation logic.
Create and maintain test frameworks and automate QA activities for data correctness, business rule validation, regression testing, and data reconciliation.
Validate Databricks pipelines (Delta tables, notebooks, jobs, workflows) and SQL transformations (Spark SQL / T-SQL); perform SQL Server object validation and execute performance testing.
Define QA standards, guidelines, and best practices for analytics engineering and reporting teams; provide production support, platform upgrade/migration QA sign-off, and maintain QA documentation; mentor team members.
Job description
Validate enterprise BI solutions aligned to Kimball dimensional modeling (facts, dimensions, conformed dimensions, SCD handling).
Work with business teams to confirm business rules, acceptance criteria, KPIs, and reconciliation logic for BI/analytics outputs.
Perform end to end QA across ingestion → transformation → semantic layer → reporting/consumption.
Analyze TPA client files and record variances.
Define QA standards, guidelines, and best practices for analytics engineering and reporting teams.
Use AI tools to build test cases, test data and testing plans wherever applicable.
Create and maintain test frameworks for:
Data correctness (row counts, aggregates, null handling, duplicates)
Business rule validation (KPI logic, exclusions, thresholds)
Regression testing (pipeline and reporting changes)
Data reconciliation (source to target, cross system checks)
Automate QA activities wherever possible to reduce manual effort and improve release velocity.
Perform QA of ETL/ELT patterns including incremental loads, CDC patterns, and partitioning strategies.
Validate SQL Server objects such as stored procedures, views, tables, indexing strategies, and job schedules where applicable.
Execute performance testing for Databricks jobs and SQL Server queries (query tuning, indexing suggestions, cluster sizing guidance).
Recommend strategies to improve platform performance, cost efficiency, and workload stability.
Provide support for production execution and delivery of BI/analytics solutions.
Support platform upgrades and migrations (Databricks runtime upgrades, cluster policies, SQL Server version changes) in dev/test, including QA sign off and documentation.
Maintain accurate and complete QA documentation: test plans, test evidence, defect logs, reconciliation results.
Ensure adherence to corporate policies, governance, and established practices.
Mentor team members on QA methods, automation, and data validation patterns.
